Artificial Intelligence (AI) has been applied to an increasing number of creative tasks from the composition of music, to painting and more recently the creation of academic texts. Reflecting on this development Harry Collins, considers how we might understand AI in the context of academic writing and warns that we should not confuse the work of algorithms with tacit complex socially constructed forms of knowledge.

Apparently there are now academic books generated by artificial intelligence algorithms. An example just published by Springer Nature, and written by ‘Beta Writer’, is called Lithium-Ion Batteries: A Machine-Generated Summary of Current Research. I don’t know anything much about Lithium-Ion batteries, nor about how these algorithms work, but I do know something about scientific knowledge and the way it is generated. I have also written three books (without the aid of an algorithm) on artificial intelligence that draw on this knowledge, most recently: Artifictional Intelligence, Against humanity’s surrender to computers.

This could be a great tool to train students on data, or even conduct research on secondary useful datasets.

« In today’s world, scientists in many disciplines and a growing number of journalists live and breathe data. There are many thousands of data repositories on the web, providing access to millions of datasets; and local and national governments around the world publish their data as well. To enable easy access to this data, we launched Dataset Search, so that scientists, data journalists, data geeks, or anyone else can find the data required for their work and their stories, or simply to satisfy their intellectual curiosity. Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s personal web page. To create Dataset search, we developed guidelines for dataset providers to describe their data in a way that Google (and other search engines) can better understand the content of their pages. These guidelines include salient information about datasets: who created the dataset, when it was published, how the data was collected, what the terms are for using the data, etc. We then collect and link this information, analyze where different versions of the same dataset might be, and find publications that may be describing or discussing the dataset. Our approach is based on an open standard for describing this information (schema.org) and anybody who publishes data can describe their dataset this way. We encourage dataset providers, large and small, to adopt this common standard so that all datasets are part of this robust ecosystem. »

Times Higher Education produced in the 3rd of April a new world ranking for 450 universities based on the United Nations Sustainable Development Goals like (gender equality, sustainable cities and communities, real-world problem solving research, etc.).

Part 1 : the diagnostic of the situation

Part 2 : the possible solutions

A great document by Corbert report in two parts about the modern science. The first part is a critical diagnostic about the situation, and the second part suggests some solutions to enhance the scientific process and make the findings more trustworthy. If interested in this topic you can also read my post on Hubbart excellent book (Corrupt research) here.

In 1942, sociologist Robert Merton articulated an ethos of science in “A Note on Science and Technology in a Democratic Order.” He argued that, although no formal scientific code exists, the values and norms of modern science can nevertheless be inferred from scientists’ common practices and widely held attitudes. Merton discussed four idealized norms: Universalism, Communality, Disinterestedness, and Organized Skepticism. In this video, we explore what these norms are and what they mean for the scientific community. « A Note on Science and Technology » can be found at: http://www.collier.sts.vt.edu/5424/pd…

Merton, Robert K. 1973. The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago Press.

Source: Berkeley Initiative for Transparency in the Social Sciences (BITSS)

The future of automated scientific writing is upon us—and that’s a good thing.

In 2014, a researcher in France revealed a disturbing fact about the published scientific literature: At least 120 computer-generated manuscripts had made their way into academic conference proceedings, according to his analysis. Those robot-written papers, containing little more than strung-together buzzwords, had been created with a piece of software known as SCIgen, originally written on a lark by a trio of MIT graduate students in 2005. But in the years since, it seemed scientists had repurposed SCIgen to puff up their resumes and boost their professional status. This was understood to be a major scandal.

For Klemen Zupancic, though, the scandal was a source of inspiration. “It got us thinking,” the 32-year-old molecular biologist and tech entrepreneur told me this week from his office in Slovenia. Zupancic is head of sciNote, a tech startup that builds tools for helping scientists to switch from using pen-and-paper laboratory notebooks to more efficient online apps. (The company claims to have about 20,000 users, of which almost half are in the U.S.) When he read about the infiltration of academic journals by robo-generated text, he realized that the same approach might be used for honest ends. If software can publish scientific gobbledygook, then maybe it can write a valid scientific paper, too. So his company set out to create a program that would do just that.

In this blog post we elaborate on the ideas behind Harold Jeffreys’s Bayes factor and illustrate this test with the Summary Statistics module in JASP.

In a previous blog post we discussed the estimation problem, where the goal was to infer, from the observed data, the magnitude of the population effect. Before studying the size of an effect, however, we arguably first need to investigate whether an effect actually exists. Here we address the existence problem with a hypothesis test and we emphasize the difference between testing and estimation.

The outline of this blog post is as follows: Firstly, we discuss a hypothesis proposed in a recent study relating fungal infections to Alzheimer’s disease. This hypothesis is then operationalized within a statistical model, and we discuss Bayesian model learning in general, before we return to the Alzheimer’s example. This is followed by a comparison of the Bayes factor to other methods of inference, and the blog post concludes with a short summary.